
Zheng Duan
Senior lecturer

Reference evapotranspiration estimation in hyper-arid regions via D-vine copula based-quantile regression and comparison with empirical approaches and machine learning models
Author
Summary, in English
Study region: Two hyper-arid regions (Atbara and Kassala stations) in Sudan. Study focus: The study aims to evaluate the potential of the D-vine Copula-based quantile regression (DVQR) model for estimating daily ETo during 2000–2015 based on various input structures. Further, the DVQR model was compared with Multivariate Linear quantile regression (MLQR), Bayesians Model Averaging quantile regression (BMAQR), Empirical Models (EMMs), and Classical Machine Learning (CML). Besides, the CML models including Random Forest (RF), Support Vector Machine (SVM), Extreme Learning Machine (ELM), Extreme Gradient Boosting (XGBoost), and M5 Model Tree (M5Tree) were employed. New hydrological insights for the region: The original EMMs showed poor performance, which improved after calibration techniques. The DVQR, MLQR, and BMAQR models showed better performance than the calibrated EMMs. However, the DVQR model exhibited the highest accuracy than the MLQR and BMAQR models over two study sites. The M5Tree, SVM, and XGBoost models perfumed better than ELM and RF models at both study sites. The DVQR and XGBoost models showed equivalent performance (R2, NSE, and WIA > 0.99, MAE, and RMSE < 0.2) to the M5Tree and SVM models, but they had significantly more accuracy than the calibrated EMMs, MLQR, BMAQR, ELM, and RF models in two hyper-arid regions. Overall, the high dimensional DVQR model is recommended as a promising alternative technique for estimating daily ETo in hyper-arid climate conditions around the world.
Department/s
- Dept of Physical Geography and Ecosystem Science
- MERGE: ModElling the Regional and Global Earth system
- BECC: Biodiversity and Ecosystem services in a Changing Climate
Publishing year
2022-12
Language
English
Publication/Series
Journal of Hydrology: Regional Studies
Volume
44
Document type
Journal article
Publisher
Elsevier
Topic
- Physical Geography
- Water Engineering
- Oceanography, Hydrology, Water Resources
Keywords
- Empirical models
- Hyper-arid region
- Machine learning
- Quantile regression
- Reference evapotranspiration
- Sudan
Status
Published
ISBN/ISSN/Other
- ISSN: 2214-5818